Hyponymy　is　used　to　build　a　taxonomic　hierarchy.　But　the　terms　in　hyponymy　may　have　multiple　senses.　It　will　cause　the　problem　of　polysemy　and　affect　the　building　of　taxonomic　hierarchy.　In　order　to　solve　the　problem,　we　present　a　method　of　sense　recognition　of　hyponymy　based　on　vector　space　model.　Firstly　we　acquire　the　contexts　of　hyponymy　from　Chinese　free　corpus.　Secondly　we　use　Cilin　to　construct　a　relation-word　vector　space.　Then　we　use　latent　semantic　analysis　to　reduce　the　dimension　of　the　vector　space.　In　the　final　phase,　we　recognize　the　senses　of　hyponymy　using　average-group　clustering.　Experimental　results　show　that　the　method　can　provide　adequate　discrimination　of　the　different　senses.